Released in October 2025, OpenAI's GPT-5 Image Mini is a multimodal model designed for those seeking efficient text and image processing. With a context window of 400,000 tokens, it combines advanced language capabilities from GPT-5 Mini with image generation features from GPT Image 1 Mini. The model supports input modalities like file, image, and text, and outputs both image and text. It excels in instruction following, text rendering, and detailed image editing, making it ideal for applications that require high-quality visual creation and strong text understanding.
Use Cases
Here are a few ways teams apply OpenAI: GPT-5 Image Mini in practice—from fast drafting to multimodal understanding. Adapt these ideas to your workflow.
Create detailed visual content efficiently
Process large-scale text and image data
Enhance applications with multimodal capabilities
Improve text rendering and image editing
Develop scalable text and image solutions
Key Features
A quick look at the capabilities that make this model useful in real projects.
Multimodal text and image processing
400,000 token context window
Advanced instruction following
Efficient image generation
Supports file, image, and text inputs
Outputs high-quality text and images
Specs
Overview
Vendor
openai
Model ID
openai/gpt-5-image-mini
Release
2025-10-16
Modalities & context
Input
file · image · text
Output
image · text
Context
400,000 tokens
Parameters & defaults
Supported parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, presence_penalty, reasoning, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs, top_p
Defaults: temperature 0.2, top_p 0.95
Benchmark tests: OpenAI: GPT-5 Image Mini
We ran this model against a few representative prompts to show its range. Review the outputs below and be the judge.
Text
Prompt:
Write 150 words on how AI might positively upend work, leisure and creativity
The OpenAI GPT-5 Image Mini is an advanced AI model designed for generating and interpreting images based on textual input. It utilizes deep learning techniques to create visual content that aligns with user-provided descriptions, making it suitable for applications in graphic design, marketing, and content creation. Typical use cases include generating illustrations for articles, creating visual assets for social media, and assisting in product design by visualizing concepts.
Notable constraints include the model's reliance on the quality and clarity of the input text, as ambiguous or vague descriptions may lead to suboptimal image outputs. Additionally, while the model can produce a wide range of images, it may not always accurately represent complex or highly specific requests. Users should also be aware of potential limitations in the model's understanding of cultural nuances and context, which can affect the relevance of generated images.
Run this prompt on Upend.AI